{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import yellowbrick as yb\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from yellowbrick.exceptions import ModelError\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "from yellowbrick.style.palettes import LINE_COLOR\n",
    "from yellowbrick.classifier.base import ClassificationScoreVisualizer\n",
    "\n",
    "from scipy import interp\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import auc, roc_curve\n",
    "\n",
    "\n",
    "# Dictionary keys for ROCAUC\n",
    "MACRO = \"macro\"\n",
    "MICRO = \"micro\"\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## ROCAUC Visualizer\n",
    "##########################################################################\n",
    "\n",
    "class ROCAUC(ClassificationScoreVisualizer):\n",
    "    \"\"\"\n",
    "    Receiver Operating Characteristic (ROC) curves are a measure of a\n",
    "    classifier's predictive quality that compares and visualizes the tradeoff\n",
    "    between the models' sensitivity and specificity. The ROC curve displays\n",
    "    the true positive rate on the Y axis and the false positive rate on the\n",
    "    X axis on both a global average and per-class basis. The ideal point is\n",
    "    therefore the top-left corner of the plot: false positives are zero and\n",
    "    true positives are one.\n",
    "\n",
    "    This leads to another metric, area under the curve  (AUC), a computation\n",
    "    of the relationship between false positives and true positives. The higher\n",
    "    the AUC, the better the model generally is. However, it is also important\n",
    "    to inspect the \"steepness\" of the curve, as this describes the\n",
    "    maximization of the true positive rate while minimizing the false positive\n",
    "    rate. Generalizing \"steepness\" usually leads to discussions about\n",
    "    convexity, which we do not get into here.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : estimator\n",
    "        Must be a classifier, otherwise raises YellowbrickTypeError\n",
    "\n",
    "    ax : matplotlib Axes, default: None\n",
    "        The axes to plot the figure on. If None is passed in the current axes\n",
    "        will be used (or generated if required).\n",
    "\n",
    "    classes : list\n",
    "        A list of class names for the legend. If classes is None and a y value\n",
    "        is passed to fit then the classes are selected from the target vector.\n",
    "        Note that the curves must be computed based on what is in the target\n",
    "        vector passed to the ``score()`` method. Class names are used for\n",
    "        labeling only and must be in the correct order to prevent confusion.\n",
    "\n",
    "    micro : bool, default = True\n",
    "        Plot the micro-averages ROC curve, computed from the sum of all true\n",
    "        positives and false positives across all classes.\n",
    "\n",
    "    macro : bool, default = True\n",
    "        Plot the macro-averages ROC curve, which simply takes the average of\n",
    "        curves across all classes.\n",
    "\n",
    "    per_class : bool, default = True\n",
    "        Plot the ROC curves for each individual class. This should be set\n",
    "        to false if only the macro or micro average curves are required.\n",
    "\n",
    "    kwargs : keyword arguments passed to the super class.\n",
    "        Currently passing in hard-coded colors for the Receiver Operating\n",
    "        Characteristic curve and the diagonal.\n",
    "        These will be refactored to a default Yellowbrick style.\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    score_ : float\n",
    "        Global accuracy score, unless micro or macro scores are requested\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    ROC curves are typically used in binary classification, and in fact the\n",
    "    Scikit-Learn ``roc_curve`` metric is only able to perform metrics for\n",
    "    binary classifiers. As a result it is necessary to binarize the output or\n",
    "    to use one-vs-rest or one-vs-all strategies of classification. The\n",
    "    visualizer does its best to handle multiple situations, but exceptions can\n",
    "    arise from unexpected models or outputs.\n",
    "\n",
    "    Another important point is the relationship of class labels specified on\n",
    "    initialization to those drawn on the curves. The classes are not used to\n",
    "    constrain ordering or filter curves; the ROC computation happens on the\n",
    "    unique values specified in the target vector to the ``score`` method. To\n",
    "    ensure the best quality visualization, do not use a LabelEncoder for this\n",
    "    and do not pass in class labels.\n",
    "\n",
    "    .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n",
    "    .. todo:: Allow the class list to filter the curves on the visualization.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> from sklearn.datasets import load_breast_cancer\n",
    "    >>> from yellowbrick.classifier import ROCAUC\n",
    "    >>> from sklearn.linear_model import LogisticRegression\n",
    "    >>> from sklearn.model_selection import train_test_split\n",
    "    >>> data = load_breast_cancer()\n",
    "    >>> X = data['data']\n",
    "    >>> y = data['target']\n",
    "    >>> X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "    >>> viz = ROCAUC(LogisticRegression())\n",
    "    >>> viz.fit(X_train, y_train)\n",
    "    >>> viz.score(X_test, y_test)\n",
    "    >>> viz.poof()\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model, ax=None, classes=None,\n",
    "                 micro=True, macro=True, per_class=True, **kwargs):\n",
    "        super(ROCAUC, self).__init__(model, ax=ax, classes=classes, **kwargs)\n",
    "\n",
    "        # Set the visual parameters for ROCAUC\n",
    "        self.micro = micro\n",
    "        self.macro = macro\n",
    "        self.per_class = per_class\n",
    "\n",
    "    def score(self, X, y=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Generates the predicted target values using the Scikit-Learn\n",
    "        estimator.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : ndarray or DataFrame of shape n x m\n",
    "            A matrix of n instances with m features\n",
    "\n",
    "        y : ndarray or Series of length n\n",
    "            An array or series of target or class values\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        score_ : float\n",
    "            Global accuracy unless micro or macro scores are requested.\n",
    "        \"\"\"\n",
    "\n",
    "        # Compute the predictions for the test data\n",
    "        y_pred = self._get_y_scores(X)\n",
    "        \n",
    "        if len(y_pred.shape) == 1:\n",
    "            self._binary_decision = True\n",
    "            \n",
    "            if self.micro or self.macro or self.per_class:\n",
    "                raise ModelError(\n",
    "                    \"Micro, macro, and per-class scores are not defined for \"\n",
    "                    \"binary classification for estimators with only \" \n",
    "                    \"decision_function methods; set micro, macro, and \"\n",
    "                    \"per-class params to False.\"\n",
    "                )\n",
    "        else:\n",
    "            self._binary_decision = False\n",
    "            if not self.micro and not self.macro and not self.per_class:\n",
    "                raise YellowbrickValueError(\n",
    "                    \"no curves will be drawn; specify micro, macro, or per_class\"\n",
    "                )\n",
    "                \n",
    "        # Classes may be label encoded so only use what's in y to compute.\n",
    "        # The self.classes_ attribute will be used as names for labels.\n",
    "        classes = np.unique(y)\n",
    "        n_classes = len(classes)\n",
    "\n",
    "        # Store the false positive rate, true positive rate and curve info.\n",
    "        self.fpr = dict()\n",
    "        self.tpr = dict()\n",
    "        self.roc_auc = dict()\n",
    "\n",
    "        # Compute ROC curve and ROC area for each class\n",
    "        if self._binary_decision == True:\n",
    "            self.fpr[0], self.tpr[0], _ = roc_curve(y, y_pred)\n",
    "            self.roc_auc[0] = auc(self.fpr[0], self.tpr[0])\n",
    "        else: \n",
    "            for i, c in enumerate(classes):\n",
    "                self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c)\n",
    "                self.roc_auc[i] = auc(self.fpr[i], self.tpr[i])\n",
    "\n",
    "        # Compute micro average\n",
    "        if self.micro:\n",
    "            self._score_micro_average(y, y_pred, classes, n_classes)\n",
    "\n",
    "        # Compute macro average\n",
    "        if self.macro:\n",
    "            self._score_macro_average(y_pred, n_classes)\n",
    "\n",
    "        # Draw the Curves\n",
    "        self.draw()\n",
    "\n",
    "        # Set score to micro average if specified\n",
    "        if self.micro:\n",
    "            self.score_ = self.roc_auc[MICRO]\n",
    "\n",
    "        # Set score to macro average if not micro\n",
    "        if self.macro:\n",
    "            self.score_ = self.roc_auc[MACRO]\n",
    "\n",
    "        # Set score to the base score if neither macro nor micro\n",
    "        self.score_ = self.estimator.score(X, y)\n",
    "\n",
    "        return self.score_\n",
    "\n",
    "    def draw(self):\n",
    "        \"\"\"\n",
    "        Renders ROC-AUC plot.\n",
    "        Called internally by score, possibly more than once\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        ax : the axis with the plotted figure\n",
    "        \"\"\"\n",
    "        colors = self.colors[0:len(self.classes_)]\n",
    "        n_classes = len(colors)\n",
    "\n",
    "        # If binary decision, plot the ROC curve\n",
    "        if self._binary_decision == True:\n",
    "            self.ax.plot(\n",
    "                self.fpr[0], self.tpr[0],\n",
    "                label='ROC for binary decision, AUC = {:0.2f}'.format(\n",
    "                        self.roc_auc[0]\n",
    "                )\n",
    "            )\n",
    "        # Otherwise plot the ROC curves for each class\n",
    "        if self.per_class:\n",
    "            for i, color in zip(range(n_classes), colors):\n",
    "                self.ax.plot(\n",
    "                    self.fpr[i], self.tpr[i], color=color,\n",
    "                    label='ROC of class {}, AUC = {:0.2f}'.format(\n",
    "                        self.classes_[i], self.roc_auc[i],\n",
    "                    )\n",
    "                )\n",
    "\n",
    "        # Plot the ROC curve for the micro average\n",
    "        if self.micro:\n",
    "            self.ax.plot(\n",
    "                self.fpr[MICRO], self.tpr[MICRO], linestyle=\"--\",\n",
    "                color= self.colors[len(self.classes_)-1],\n",
    "                label='micro-average ROC curve, AUC = {:0.2f}'.format(\n",
    "                    self.roc_auc[\"micro\"],\n",
    "                )\n",
    "            )\n",
    "\n",
    "        # Plot the ROC curve for the macro average\n",
    "        if self.macro:\n",
    "            self.ax.plot(\n",
    "                self.fpr[MACRO], self.tpr[MACRO], linestyle=\"--\",\n",
    "                color= self.colors[len(self.classes_)-1],\n",
    "                label='macro-average ROC curve, AUC = {:0.2f}'.format(\n",
    "                    self.roc_auc[\"macro\"],\n",
    "                )\n",
    "            )\n",
    "\n",
    "        # Plot the line of no discrimination to compare the curve to.\n",
    "        self.ax.plot([0,1], [0,1], linestyle=':', c=LINE_COLOR)\n",
    "        return self.ax\n",
    "\n",
    "    def finalize(self, **kwargs):\n",
    "        \"\"\"\n",
    "        Finalize executes any subclass-specific axes finalization steps.\n",
    "        The user calls poof and poof calls finalize.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        kwargs: generic keyword arguments.\n",
    "\n",
    "        \"\"\"\n",
    "        # Set the title and add the legend\n",
    "        self.set_title('ROC Curves for {}'.format(self.name))\n",
    "        self.ax.legend(loc='lower right', frameon=True)\n",
    "\n",
    "        # Set the limits for the ROC/AUC (always between 0 and 1)\n",
    "        self.ax.set_xlim([0.0, 1.0])\n",
    "        self.ax.set_ylim([0.0, 1.0])\n",
    "\n",
    "        # Set x and y axis labels\n",
    "        self.ax.set_ylabel('True Postive Rate')\n",
    "        self.ax.set_xlabel('False Positive Rate')\n",
    "\n",
    "    def _get_y_scores(self, X):\n",
    "        \"\"\"\n",
    "        The ``roc_curve`` metric requires target scores that can either be the\n",
    "        probability estimates of the positive class, confidence values or non-\n",
    "        thresholded measure of decisions (as returned by \"decision_function\").\n",
    "\n",
    "        This method computes the scores by resolving the estimator methods\n",
    "        that retreive these values.\n",
    "\n",
    "        .. todo:: implement confidence values metric.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : ndarray or DataFrame of shape n x m\n",
    "            A matrix of n instances with m features -- generally the test data\n",
    "            that is associated with y_true values.\n",
    "        \"\"\"\n",
    "        # The resolution order of scoring functions\n",
    "        attrs = (\n",
    "            'predict_proba',\n",
    "            'decision_function',\n",
    "        )\n",
    "\n",
    "        # Return the first resolved function\n",
    "        for attr in attrs:\n",
    "            try:\n",
    "                method = getattr(self.estimator, attr, None)\n",
    "                if method:\n",
    "                    return method(X)\n",
    "            except AttributeError:\n",
    "                # Some Scikit-Learn estimators have both probability and\n",
    "                # decision functions but override __getattr__ and raise an\n",
    "                # AttributeError on access.\n",
    "                continue\n",
    "\n",
    "        # If we've gotten this far, raise an error\n",
    "        raise ModelError(\n",
    "            \"ROCAUC requires estimators with predict_proba or \"\n",
    "            \"decision_function methods.\"\n",
    "        )\n",
    "\n",
    "    def _score_micro_average(self, y, y_pred, classes, n_classes):\n",
    "        \"\"\"\n",
    "        Compute the micro average scores for the ROCAUC curves.\n",
    "        \"\"\"\n",
    "        # Convert y to binarized array for micro and macro scores\n",
    "        y = label_binarize(y, classes=classes)\n",
    "        if n_classes == 2:\n",
    "            y = np.hstack((1-y, y))\n",
    "\n",
    "        # Compute micro-average\n",
    "        self.fpr[MICRO], self.tpr[MICRO], _ = roc_curve(y.ravel(), y_pred.ravel())\n",
    "        self.roc_auc[MICRO] = auc(self.fpr[MICRO], self.tpr[MICRO])\n",
    "\n",
    "    def _score_macro_average(self, y_pred, n_classes):\n",
    "        \"\"\"\n",
    "        Compute the macro average scores for the ROCAUC curves.\n",
    "        \"\"\"   \n",
    "        # Gather all FPRs\n",
    "        all_fpr = np.unique(np.concatenate([self.fpr[i] for i in range(n_classes)]))\n",
    "        avg_tpr = np.zeros_like(all_fpr)\n",
    "\n",
    "        # Compute the averages per class\n",
    "        for i in range(n_classes):\n",
    "            avg_tpr += interp(all_fpr, self.fpr[i], self.tpr[i])\n",
    "\n",
    "        # Finalize the average\n",
    "        avg_tpr /= n_classes\n",
    "\n",
    "        # Store the macro averages\n",
    "        self.fpr[MACRO] = all_fpr\n",
    "        self.tpr[MACRO] = avg_tpr\n",
    "        self.roc_auc[MACRO] = auc(self.fpr[MACRO], self.tpr[MACRO])\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## Quick method for ROCAUC\n",
    "##########################################################################\n",
    "\n",
    "def roc_auc(model, X, y=None, ax=None, **kwargs):\n",
    "    \"\"\"ROCAUC Quick method:\n",
    "\n",
    "    Receiver Operating Characteristic (ROC) curves are a measure of a\n",
    "    classifier's predictive quality that compares and visualizes the tradeoff\n",
    "    between the models' sensitivity and specificity. The ROC curve displays\n",
    "    the true positive rate on the Y axis and the false positive rate on the\n",
    "    X axis on both a global average and per-class basis. The ideal point is\n",
    "    therefore the top-left corner of the plot: false positives are zero and\n",
    "    true positives are one.\n",
    "\n",
    "    This leads to another metric, area under the curve  (AUC), a computation\n",
    "    of the relationship between false positives and true positives. The higher\n",
    "    the AUC, the better the model generally is. However, it is also important\n",
    "    to inspect the \"steepness\" of the curve, as this describes the\n",
    "    maximization of the true positive rate while minimizing the false positive\n",
    "    rate. Generalizing \"steepness\" usually leads to discussions about\n",
    "    convexity, which we do not get into here.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : the Scikit-Learn estimator\n",
    "        Should be an instance of a classifier, else the __init__ will\n",
    "        return an error.\n",
    "\n",
    "    X : ndarray or DataFrame of shape n x m\n",
    "        A matrix of n instances with m features\n",
    "\n",
    "    y : ndarray or Series of length n\n",
    "        An array or series of target or class values\n",
    "\n",
    "    ax : the axis to plot the figure on.\n",
    "\n",
    "    classes : list\n",
    "        A list of class names for the legend. If classes is None and a y value\n",
    "        is passed to fit then the classes are selected from the target vector.\n",
    "        Note that the curves must be computed based on what is in the target\n",
    "        vector passed to the ``score()`` method. Class names are used for\n",
    "        labeling only and must be in the correct order to prevent confusion.\n",
    "\n",
    "    micro : bool, default = True\n",
    "        Plot the micro-averages ROC curve, computed from the sum of all true\n",
    "        positives and false positives across all classes.\n",
    "\n",
    "    macro : bool, default = True\n",
    "        Plot the macro-averages ROC curve, which simply takes the average of\n",
    "        curves across all classes.\n",
    "\n",
    "    per_class : bool, default = True\n",
    "        Plot the ROC curves for each individual class. Primarily this is set\n",
    "        to false if only the macro or micro average curves are required.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    ROC curves are typically used in binary classification, and in fact the\n",
    "    Scikit-Learn ``roc_curve`` metric is only able to perform metrics for\n",
    "    binary classifiers. As a result it is necessary to binarize the output or\n",
    "    to use one-vs-rest or one-vs-all strategies of classification. The\n",
    "    visualizer does its best to handle multiple situations, but exceptions can\n",
    "    arise from unexpected models or outputs.\n",
    "\n",
    "    Another important point is the relationship of class labels specified on\n",
    "    initialization to those drawn on the curves. The classes are not used to\n",
    "    constrain ordering or filter curves; the ROC computation happens on the\n",
    "    unique values specified in the target vector to the ``score`` method. To\n",
    "    ensure the best quality visualization, do not use a LabelEncoder for this\n",
    "    and do not pass in class labels.\n",
    "\n",
    "    .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n",
    "    .. todo:: Allow the class list to filter the curves on the visualization.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> from sklearn.datasets import load_breast_cancer\n",
    "    >>> from yellowbrick.classifier import roc_auc\n",
    "    >>> from sklearn.linear_model import LogisticRegression\n",
    "    >>> data = load_breast_cancer()\n",
    "    >>> roc_auc(LogisticRegression(), data.data, data.target)\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : matplotlib axes\n",
    "        Returns the axes that the roc-auc curve was drawn on.\n",
    "    \"\"\"\n",
    "    # Instantiate the visualizer\n",
    "    visualizer = ROCAUC(model, ax, **kwargs)\n",
    "\n",
    "    # Create the train and test splits\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "    # Fit and transform the visualizer (calls draw)\n",
    "    visualizer.fit(X_train, y_train, **kwargs)\n",
    "    visualizer.score(X_test, y_test)\n",
    "    visualizer.finalize()\n",
    "\n",
    "    # Return the axes object on the visualizer\n",
    "    return visualizer.ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy = pd.read_csv('data/occupancy/occupancy.csv')\n",
    "features = [\n",
    "    \"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"\n",
    "]\n",
    "classes = [\"unoccupied\", \"occupied\"]\n",
    "X = occupancy[features]\n",
    "y = occupancy['occupancy']\n",
    "\n",
    "# Create the train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.svm import LinearSVC, NuSVC, SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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hPj4+oL+/Ny2LiwK5LO666y4WLVpE27Ztad68eUCXhbfExES2bt2aq2392ay0CugI4O5z2OK17BRwDkgUkfPAn0DpnHb46pKtOnSGUgqA5ORkz+fnn3+eF154gcaNG9sYUdHiz+QwDzhvjFkNvAU8YYwZYozpIiIrgPXAWmPMGmAnsCinHS7cfgDQoTOUCnRxcXHExsZy8OBBwGpVGDx4MCEhend+XvFbSYpIKvDwJbN3eC1/AXjhcverQ2copc6dO8eff/7Jtm3bqFy5st3hFEk6fIZSqsBLSUlh9uzZXLhgdVv26dOH9evXc/PNN9scWdGlyUEpVeBNmDCBRx55hEmTJgHW7aqVKlWyOaqiTRvolFIFUmpqKkFB1vXrAw88wMGDB7nrrrtsjipwaM1BKVXgbNmyhdatW7Nq1SrAGkn1zTffpEKFCjZHFjg0OSilCpzExES2b9/uSQ4q/2mzklKqQFi1ahVRUVFUqFCBRo0asX79emrUqGF3WAFLaw5KKdstX76czp07M3z4cM88TQz20pqDUso2LpcLh8NB8+bN6dWrF/fdd5/dISk3rTkopfLdyZMneeihh/jggw8ACA4O5p133qFRo0Y5bKnyiyYHpVS+u3DhAosXL2bBggW4XC67w1GZ0GYlpVS+OHToEKdPn6Zu3bpUrFiRr776iqioKBwOh92hqUxoclBK+d3x48dp3rw5V199NcuWLcPpdBIdHW13WCobmhyUUn5Xvnx5+vTpQ61atQgNDbU7HOUDTQ5KqTyXkpLCpEmT2LdvH2PHjgVg1KhRNkelLod2SCul8lxqaipz5sxh3rx5nDhxwu5wVC5ozUEplSeSkpLYvn07DRo0IDQ0lKlTp1K6dGnKlStnd2gqFzQ5KKWumMvlokuXLuzYsYM1a9bwj3/8g9q1a9sdlroCmhyUUlfM4XBw9913s3nzZiIiIuwOR+WBHJODMcYJDAUMMBB4HHhNRJL8HJtSqgBbsWIFH374IZMnTyYkJESHvihifOmQfheIAGKAC0BtYKo/g1JKFXwzZ87kiy++YN26dXaHovzAl+QQKyLDgWQROQvcCzT0b1hKqYJo+/btns9jxoxh0aJFNG/e3MaIlL/4khxc7qaltAFQynt9VkoFiJdffpkWLVqwdu1aAMqVK0fDhnqdWFT5khzeBhYDVxljJgAbgAl+jUopVeDcfPPN3HDDDZQpU8buUFQ+yDE5iMhHwMPAGGAP0FlEtM9BqSLu4MGDDBgwwPMQW5MmTVi4cCHGGJsjU/nBl7uVPheR7sA2r3lLROQmv0amlLLVF198waeffooxhscffxxAR1ANIFkmB2PMPKABcLUxZs8l2+z3d2BKqfy3b98+qlSpQlBQEP3796dy5cp06dLF7rCUDbJrVroXaAt8C7Tx+tcUaOX/0JRS+enrr7+mSZMmTJ8+HYCQkBC6du2qtYUAlWXNQUROA6eBrsaYhkAJwAEEA7cC0/IlQqVUvoiJiaFq1apUqFDB7lBUAeBLn8N0oBlQFtgOXA+sQpODUoVaYmIi48ePp1OnTjRo0ICrrrqKNWvWEBSkgzUr325lbQlcC8QB/YHGgNOfQSml/C8+Pp433njD874FQBOD8vDlN+GQiCRj1RquE5FfgEj/hqWU8oeEhAT+/vtvAJo1a8bkyZOZPHmyzVGpgsiX5HDQGDMMWA08ZIy5G6v/QSlViOzZs4cWLVowcuRIz7wePXoQGanXeiojX5LDA8BvIrIemAvcg/VQnFKqEKlSpQolS5akbNmyuFw6Ao7KXrYd0saYEsB5EZkNICL/McZ8AAwBvvd/eEqpK7FgwQJSUlLo0qULTqeTxYsXExoaandYqhDIsuZgjHkIOAn8YYyJcc+7C9gB9M6f8JRSuXXs2DEefvhhnn32WRITEwE0MSifZVdzeBq4AbgGeNYYcxZoD7wATMmH2JRSl8nlcnH69GlKlSpFhQoVeO+99zDGEBYWZndoqpDJLjkkiMgmYJO7KWkJEOV+OC5Hxpgg4D2sITgSgX4isstreQesROMA4oFHRUQbQpXKpaSkJHr27Mnp06f5+uuvCQ4OpnPnznaHpQqp7DqkU7w+nwL6+JoY3G4HiolIU+BZYHzaAmNMJPA6cJuINAZ+x3pPhFIql5xOJyVLliQiIsJzu6pSuZVdzcH7Kv6M+1mHy9EC+AZARNYaYxp5LWsGbAHGG2NqAlNE5FhOO0xOSiYp6QLx8fGXGUrRo2VwUSCXxYEDB/j555+57bbbAOjbty9hYWHs3r3b5sjsF8i/F3khu+RQxxizNJPPAIhI2xz2XRL4y2s6xRgTIiIXsGoJbbCG4jgDrDDGrBGRndntMNQZijMliNjY2BwOXbTFx8cHfBmkCeSycLlcDB48mB07dtCrVy9Onz6tr+x0C+TfC2+JiYls3bo1V9tmlxxuy104HqdJ/yR1kDsxAJwA1ovIEQBjzHKsRJFtclBKwdmzZylevDgOh4M33niDo0ePUqdOHb1SVnkqu1FZf7jCfa8COgNzjDFNsJqR0mwE6htjygN/Ak2AD67weEoVeePGjeOjjz5i5cqVlC5dmiZNmtgdkiqichyV9QrMA242xqzGuiPpfmPMEGCXiMx3D8nxrXvdOSKSu7qPUgHE6XQSHBzM/v37KV26tN3hqCLMb8lBRFLJOMzGDq/ls4HZ/jq+UkXBmTNn+OSTT3jwwQdxOBw8+uij9OvXjxIldHgz5V8+JQdjTA2gHtbdR9VE5Dd/BqWUsowYMYKPPvqIMmXK0KNHD0JDQ/UpZ5UvfHnZz13ACKA41itC1xhjnhKRj/0dnFKBKDk52ZMAhg4dSsWKFfVhNpXvfBmV9Rms5xJOi8hRoCEwzK9RKRWgFi1aRExMjOf2w8qVKzN8+HCKFStmc2Qq0PiSHFJExPO4pYgcBlL9F5JSge3EiRNs27bN7jBUgPOlz+EXY8xAINQYcz3wCPCzf8NSKjC4XC4+++wz2rdvT2RkJDfffDM//fQTlSpVsjs0FeB8qTk8ClQGzgHTsB5ue8SfQSkVKD755BMeeughXn31Vc88TQyqIPCl5vAgMEFEtJ9BqTyQmpqKw+HA4XBw55138tNPPzFgwAC7w1IqHV9qDpWBtcaYb4wx/88YU9zfQSlVVO3Zs4dOnToxd+5cAMLCwnjjjTeoWrWqzZEplV6OyUFEhorINcAYrGEufjbGzPB7ZEoVQQ6Hg82bN7N69Wq7Q1EqW74+BOcAQgEn1p1Kif4MSqmiZNOmTZQoUYJatWpxzTXXsGrVKmrUqGF3WEplK8eagzHmP8A+4HGst8FdLyL9/B2YUkXB9u3badeuHYMGDcLlsl6RoolBFQa+1Bx2AjG+vIxHKWVxuVw4HA6io6Pp168ft956Kw6Hw+6wlPJZlsnBGNNfRN4HygIDjDHplovIKD/HplShc+bMGV566SUiIyMZOXIkQLrbVJUqLLJrVnJc8tn7n1IqEw6HgyVLlvDdd9+RlJRkdzhK5Vp2L/uZ7P74u4hM915mjHnUr1EpVYicPHmS33//nZiYGCIiIoiLi6NKlSo4nU67Q1Mq17JrVnoc6z3QDxtjql+yTW/gXT/HplSBd/78eVq1akVKSgpr1qyhVKlS1KpVy+6wlLpi2XVI7wJiydiUlAjc58eYsnTwr3N6NaYKlGLFitG/f38AIiIibI5GqbyTXbPSV8BXxpg5IrIdwBhTEqgqIr/kV4CXurNB9ZxXUspPXC4XH3/8McuWLWPq1Kk4HA4ee+wxu8NSKs/5MnxGM2PMNGNMBWAb8JkxZrSf48pU5VLhjOsca8ehlfKYP38+ixcvZvfu3XaHopTf+JIcHgGeAu4BvgD+CbT3Z1BKFSQpKSls2LABsO5GmjBhAqtXr6Z27do2R6aU//iSHBCRk0BHYIGIXADC/RqVUgXIAw88QKdOndixYwdgvZ2tSpUqNkellH/5+rKfr4CawGJjzBxgvX/DUqrguOeeewgODqZcuXJ2h6JUvvGl5tAXGAc0FpEkYAagYyupIuunn36id+/eJCQkAHDrrbcydepUKlSoYHNkSuUfX5KDE7gNWGSM+RloC4T5NSqlbPTll1+ycOFCvvvuO7tDUco2viSHd4DiWDWIe7GG7p7kz6CUym/btm3zjJo6dOhQFixYwB133GFzVErZx5fkECsiA0Vks4hsEpGBWA/HKVUkTJ48mRYtWjB//nwAwsPDadq0qc1RKWUvX5JDkDGmdNqE+/MF/4WkVP666aabaNiwob6qUykvviSHN4H1xpjxxpjxWHcqTfBvWEr5z4kTJ3j00Uf57bffAKhduzaLFy8mJibG5siUKjh8eYf0/4A7gD3A70A3EZnm57iU8psVK1Ywa9YsJk+e7JmnL+JRKr3sRmUNAh4FooCVIqKjsKpC69ChQ5QtW5ZixYrRtWtXpk+fTseOHe0OS6kCK7uaw3tADyABGG6MGZk/ISmVt9auXUvTpk0ZP348YNUSOnfuTHBwsM2RKVVwZZccWgGtRORZrGcbuudPSErlrfr161OtWjWqV9cRfZXyVXbDZ5wXEReAiJwwxrjyKSalrkhKSgr//e9/qVu3Lu3ataNEiRL88MMPBAX5NJSYUorsk8OlySDVn4EolVd2797NqFGjqFevHjfddBMOh0MTg1KXKbvkUN0YMy2raRHp67+wlLo8SUlJnDlzhrJlyxIVFcXUqVNp1qyZ3oWkVC5llxyGXDL9w+Xs2H2303tAA6xXi/YTkV2ZrLMA+EJEdEgOlSvHjh2ja9euVKtWjVmzZnk6nJVSuZfda0KnX+G+bweKiUhTY0wTYDzQ9ZJ1RgNlrvA4KsCVL1+eq666iipVqpCcnKzvGVcqD/jyPofcagF8AyAia40xjbwXGmPuxOrH+MaPMagiavny5fz222/Ur18fh8PBp59+SmhoqN1hKVVk+DM5lAT+8ppOMcaEiMgFY0x9oBdwJ+Dz8xPJScnEx8fncZiFUyCXQ1JSEn379uXs2bPMnDkzoMviUloWF2lZXBmfkoMxJgKoBWwBiotIgg+bnQYivaaD3K8YBfg3UBlYCtQAkowxv4tItrWIUGcosbE6IGx8fHxAlsOff/5J6dLWGJBTpkyhVKlSpKamBmRZZCZQfy8yo2VhSUxMZOvWrbnaNsf7+4wxNwGbgC+Aq4DfjTG3+LDvVVjvncbd57AlbYGIPC0ijUWkNfAh8GZOiUEFLpfLRf/+/WnXrh1nz54FoHXr1jRs2NDmyJQquny5+fsVrP6DP0XkMNaT06/7sN084LwxZjXwFvCEMWaIMaZLrqNVAcnhcFCpUiXKli3LqVOn7A5HqYDgS7NSkIgcMcYAICLb0j5nR0RSgYcvmb0jk/Ve9CEGFWAOHDjAvHnzeOyxxwAYMWIEISEhOh6SUvnEl+RwwBhzG+Byv+jnUWCff8NSge6JJ55gyZIlxMTE0Lx5c8LC9LXlSuUnX5LDQ8DbQFWsdzosAfr7MygVmBISEoiIiABgzJgxdO3alWbNmtkclVKBKcfkICJHgXvyIRYVwKZNm8arr77KkiVLqFatGlFRUURFRdkdllIBK8fkYIz5jYyD8CEiNf0SkQpIJUqUICgoiH379lGtWjW7w1Eq4PnSrNTa63Mo1itDtQFYXZHExESmTZvGAw88gNPppEePHrRv356SJUvaHZpSCt+alfZeMut1Y8wGrHGRlMqVCRMmMHbsWJKTkxk0aBAOh0MTg1IFiC/NSi29Jh1APSDcbxGpIispKckzKN6AAQNISkri/vvvtzkqpVRmfHkI7iWvfy9gNTPd68eYVBG0bt06mjRpwtKlSwEoWbIkzz//PJGRkTlsqZSygy99DnNE5L9+j0QVaeHh4Rw5coQdO3bQtm1bu8NRSuXAl5rDo36PQhVJCxYs4PDhwwBcd911/PzzzzzyyCM2R6WU8oUvNYf9xpilwDrgXNpMERnlt6hUobdkyRL69OlD165d+d///gdAxYoVbY5KKeUrX5LDWq/P+kJelSWXy4XL5SIoKIg2bdowYMAA7r1Xu6eUKoyyTA7GmHtFZLqIvJSfAanC6ciRIzz22GO0bNmSxx57jKCgIMaMGWN3WEqpXMquz2FwvkWhCj0TWAvYAAAbW0lEQVSn08nmzZtZt24dLleGB+qVUoWMP18Tqoq4X3/9lYSEBK6//nrKli3LokWLqFq1Kg6Htj4qVdhllxzqGWP2ZDLfAbh0bKXAdvToUdq0acPVV1/NypUrcTqdOiaSUkVIdslhF+7XfCqVxuVy4XA4qFixIoMHD8YY43nqWSlVdGSXHJIyGVdJBaikpCTGjRvH4cOHeffddwEYOnSozVEppfwlu+SwKt+iUAVecHAw33//PceOHePUqVOUKVPG7pCUUn6UZXIQkYH5GYgqeM6cOcOWLVto2rQpwcHB/O9//6NMmTKUKFHC7tCUUn6mdyupTLlcLm677Tb27NnD6tWrqVKlClWrVrU7LKVUPtHkoDLlcDgYMGAAu3btonz58naHo5TKZ5oclMf8+fOZPn06s2fPJjQ0lLvuusvukJRSNvFlVFYVIJYsWcLq1avZuHGj3aEopWymySGAuVwu1q69OK7iqFGjWL58OY0bN7YxKqVUQaDJIYANGzaMjh07smLFCgBKlSpFnTp1bI5KKVUQaJ9DAOvZsyd79+7lmmuusTsUpVQBozWHACIi3H333Rw9ehSAmJgYZs2aRZUqVWyOTClV0GhyCCArVqzgu+++Y+7cuXaHopQq4LRZqYjbtm0bxhiCg4Pp27cvUVFRtGzZ0u6wlFIFnNYcirC5c+fSqlUr3n//fQCCgoI0MSilfKLJoQhr2bIlDRo04Nprr7U7FKVUIaPJoQg5ffo0Q4cOZf369QCUL1+eRYsW0apVK5sjU0oVNpocipBt27YxdepU3nnnHc88fWWnUio3tEO6kDt58iRBQUGULl2aJk2a8Mknn9CmTRu7w1JKFXJacyjERISmTZsyfPhwz7z27dsTFhZmY1RKqaLAbzUHY0wQ8B7QAEgE+onILq/lTwB3uye/FpGX/BVLUVWrVi1q1apFdHS0593OSimVF/zZrHQ7UExEmhpjmgDjga4AxpiaQG+gMZAKrDTGzBORzX6Mp9BzuVx8/PHHHDx4kNjYWEJCQvjqq68ICtIKoFIqb/kzObQAvgEQkbXGmEZey/YD7UUkBcAYEwqcz2mHyUnJxMfH+yPWQuHUqVMMGzaMiIgIWrduTWhoqN0hFQiB/DtxKS2Li7Qsrow/k0NJ4C+v6RRjTIiIXBCRZOC4McYBvA78JCI7c9phqDOU2NhYP4VbMKWkpHDixAkqVqwIwPTp00lOTqZJkyY2R1YwxMfHB9zvRFa0LC7SsrAkJiaydevWXG3rz/aI00Ck97FE5ELahDGmGDDTvc4jfoyj0Dp37hwdOnTg7rvv5sIFq+huuukmKlSoYHNkSqmizp81h1VAZ2COu89hS9oCd43hC2CpiIz1YwyFWnh4OHXq1CExMZGzZ89SsmRJu0NSSgUIfyaHecDNxpjVgAO43xgzBNgFBAOtgDBjTAf3+sNEZI0f4ykUNm7cyMqVKxk0aBAAEyZM0L4FpVS+81tyEJFU4OFLZu/w+lzMX8curFJTUxk8eDDbtm2jU6dO1KpVSxODUsoW+oR0AXDq1CnKlClDUFAQEydO5MyZM9SqVcvusJRSAUxvkLfZiBEjaNy4McePHwegYcOG3HjjjTZHpZQKdJocbFa5cmUqVKjAiRMn7A5FKaU8ClVy6BBd+N91fPz4ccaNG0dqaioA/fv3Z9myZRhjbI5MKaUuKlTJYdhN9e0O4YqNGjWK1157jXnz5gEQHByM0+m0OSqllEpPO6TzwZkzZyhRogRg9THUq1eP22+/3eaolFIqa4Wq5lAYzZs3j+uuu46NGzcCULFiRR566CGCg4NtjkwppbKmycHPypcvj8vl4uDBg3aHopRSPtPkkMcuXLjA+++/z19/WWMO3njjjWzatInOnTvbHJlSSvlOk0MemzlzJs8++yyvvfaaZ56OiaSUKmy0QzoPJCUlERoaisPhoFevXuzfv58BAwbYHZZSSuWa1hyu0LZt22jdujWzZs0CIDQ0lBEjRlCuXDmbI1NKqdzT5HCFSpYsycGDB9m1a1fOKyulVCGhzUq5sHz5cipUqEB0dDRVqlQhPj6e8uXL2x2WUkrlGa05XKZt27Zx++238/jjj+NyuQA0MSilihytOfgoJSWF4OBgrr32WoYOHUr79u1xOBx2hxWQLly44BmbCqwbApRFy+KiQCqLoKAgQkLy9nSuySEHf/31F0OGDKFixYq8+uqrAAwbNszmqALX33//TXBwsOcPQd97cZGWxUWBVhZJSUmcO3eOyMjIPNunJocchIWFsWXLFsqWLUtycrK+mc1GFy5cIDg4mOLFi3vmJScn68CFbloWFwVaWTidTs6ePcuFCxfyrAahySETBw4cYO/evTRv3pxixYoxb948rrrqKh0PyWapqal5XnVWqqgIDg5O19x6pfQv7RLnzp2jXbt2uFwufvzxR0qVKkXlypXtDksppbKV132gmhzcXC4XDoeD8PBwnn76acLCwnTYC6VUwAr4W1lTU1OZOHEiPXv29FTJ+vbtS+/evfVuJJXBunXraNq0KX369KFPnz5069aNQYMGee6MOXnyJM888wx9+vShV69ePPnkkxw7dsyz/YYNG7j//vvp06cP3bt3Z+bMmRmOsX//ftq3b88zzzxz2fE1b948w7y5c+eyZMmSy95Xbr3xxhvMnTv3srYZM2YMhw4dynTZ8uXL+fTTT/MiNI8PPviAFi1akJiY6Jn37LPPsnz58nTreZfn4sWLPT/3Hj168M033+Tq2HPmzKFbt2707NmTZcuWZVi+cuVKbr/9du655x7ee+89wOpwfvLJJ+nZsyd9+/bl999/z9WxL0fA1xwcDgc//vgjmzdvZt++fdSoUcPukJSPnv4ynriff8/TJH5ng+qM6xyb7TpNmjThrbfe8kw/+eSTLF26lFtvvZWBAwfSt29f2rVrB8Dq1at56KGHiIuL49ChQ4wePZopU6ZQvnx5zp8/z7///W+qVq1Ky5YtPfuLj4+ndevWPPvss3nynbp165Yn+/Gn5557Lstl3mWTV+bPn0/Hjh1ZsGCBT+WzceNGPvzwQyZPnkxERASnTp3irrvuonbt2tSuXdvn4x47dowZM2bw+eefk5iYSK9evWjevLmn8zw1NZURI0YwY8YMqlatylNPPcWGDRvYsWMHxYsXZ86cOezZs4eXX36ZqVOn5vr7+yIgk0NiYiJr1qyhdevWOBwO3nrrLYKDgylbtqzdoalCJikpiaNHj1KqVCm2bt1KZGSkJzEANGvWjGrVqrF+/Xo2bNjA7bff7nloslixYkydOjXd3VeHDh1i0qRJnD9/nmrVqnH99dfz8ssvExwcTFhYGC+//DKpqakMGDCA0qVL07JlSx588MF08TzxxBMcPnwYYwwvvvgi77zzDuXLl6dmzZp88MEHhIaGcuDAATp27MiAAQPYuXMnr732GikpKZw6dYoXX3yRmJgY2rRpQ82aNalVqxbLli0jLi6O0qVL88knn5CQkJDuuN9++y3//e9/PXf11axZE4Dx48ezYcMGUlNTue++++jQoQObNm3ilVdeITU1lUqVKvHGG2/w4IMP8uKLL/Lnn38yduxYQkJCCA8P5+233+a7775jz549PPXUU0ybNo0FCxYQEhJCo0aNGDp0KP/5z384cOAAJ06c4NChQwwbNoyYmJgsf2br1q2jWrVq3H333QwdOtSn5BAXF8e9995LREQEAGXKlCEuLi5D0/Nzzz3Hvn37PNOlSpXinXfe8Uxv3ryZhg0b4nQ6cTqdVKtWjR07dnDdddcBcOrUKUqWLEnVqlUBiImJYePGjRw6dMiTJGvWrMnu3btzjPlKBWRy6NOnD8uWLWPp0qX885//pEKFCnaHpHJhXOdYXmhb1/MHm1/Wrl1Lnz59OHHiBEFBQfTs2ZOmTZvy9ddfe/6ovVWtWpVDhw5x9OhR6tatm27ZpfelX3311fTv3589e/bQq1cvunXrxpgxY4iOjmbx4sW89tprPP300xw7dozPP/88w+2a58+f56mnnqJy5coMHjyYpUuXplt+6NAh5s+fT1JSEjfeeCMDBgxg165dPPPMMxhj+PLLL5k7dy4xMTEcPnyYuXPnUqZMGUqUKMGCBQvo3bs38+fPT3fCS05O5rXXXmPu3LmULl2a/v37A/DDDz9w4MABZs2aRWJiIj179qR58+aMHDmSN998k1q1ahEXF5fuRLd48WI6dOjAvffey9KlSzl9+rRnmYiwcOFCZs+eTUhICI899pinWcbpdDJlyhRWrVrFtGnTsk0OcXFx9OjRg5o1a+J0Otm0aRMNGjTIdN20WunRo0cz/GxLlSqVYf0xY8ZkeVywXhns/TOPiIjgzJkznumyZcty/vx5du/eTY0aNVi+fDl169YlOjqaZcuW0a5dOzZt2sQff/zheTDXXwIyOTz88MNcc8012oSkciWtWenUqVP07duXKlWqAFCpUqVM3/i3d+9emjVrxtGjRzly5Ei6ZTt27CA1NZVrr70202MdPXqU6OhoAG644QbGjx8PQJUqVTK9j//qq6/23F3XsGFDfvvtt3TLo6KiCAkJISQkhGLFigHWq2vfe+89ihUrRkJCgud952XKlKFMmTIAdO/enSFDhnDDDTdQvnz5dEPGnDx5klKlSnnWbdiwIQA7d+7kl19+oU+fPoD1nMrBgwc5fvy45yG1Hj16pIvv4YcfZtKkSdx7771UqlTJc0UNsGfPHho0aOB51qhRo0b8+uuvAJ4yuuqqq7J9Mvqvv/5i+fLlnDx5khkzZnDmzBk+/vhjGjRoQFhYWIZtL1y44CnXw4cPp0vuaWOqVa9e3TMvp5pDiRIlSEhI8EwnJCSkSxYOh4Nx48bx4osv4nQ6iYqKokyZMnTv3p3du3fTq1cvYmJiqFevnt9vrQ+IDunvv/+eTp06ea5C2rZty9ixY/P0aUIVeMqUKcPrr7/OiBEjOHr0KDExMRw/fjzd1fry5cvZu3cv//rXv7jtttuIi4vj5MmTgHViGDlyZLoO60tVrFiRHTt2ALB+/XrPBU1QUOZ/ukeOHOHo0aOA1U5ep06ddMsz658ZM2YMgwYNYuzYsURFRXnGDPM+RuXKlYmMjGTSpEnceeed6bYvV64cp0+f9nyvLVu2AFbzR+PGjZkxYwbTp0+nQ4cOVK1alYoVK3o6VN9//30WLVrk2df8+fO54447mDFjBnXq1GHOnDmeZTVr1mTz5s1cuHABl8vF+vXrueaaa7L8XpmZP38+3bt3Z9q0aUydOpU5c+awatUqTp48Sb169dLFsmHDBk9/Qrdu3Zg6dSpnz54F4MSJEwwfPpxz585lKMsZM2Z4/nknBoDrrruO+Ph4EhMT+fvvv9m9ezdRUVHp1lm5ciVTp05lypQp7Nu3j2bNmrFlyxaaNm3KrFmzaN++faY11LwWEDWHdevW8eOPP7Jy5Uo6duxodziqCKlduzZ9+vRh9OjRTJw4kUmTJvHKK68wefJkwLqSff/99wkODqZKlSoMHTqUgQMHEhwcTEJCAnfeeSetWrXKcv+jR4/m5ZdfxuVyERwczCuvvJJtPKVLl2b06NH88ccfNGzYkFatWrF58+Zst+nSpQuDBw+mZMmSXHXVVZw6dSrT9Xr27Mno0aN5/fXX080PCQlh5MiRPPDAA5QqVcrzoGLbtm358ccf6dWrF2fPnqVdu3aUKFGCl156ieHDhxMUFESFChW47777+OijjwDr5DlixAjCw8MJCgpi1KhRrF+/HgBjDB06dOCee+4hNTWV2NhY2rVr50mel3r//fepW7duug7tuLg4xo0b55kODw/nlltuYc6cOTzwwANs376drl27EhERQWhoKKNGjQKs2lDanUIhISGcP3+eIUOGZGgmzEmFChU8d7K5XC6eeOIJwsLCWLNmDfHx8QwcOJCKFSvSo0cPihUrRufOnalTpw4nT57k7bffZtKkSURGRubYfJUXHGlXCQVZfHx8DeC3+vXrExYW5tM2a9asoUmTJjgcDpKSkvj111+pV6+eX+PML/Hx8cTGZn9HTVGUVuX3bk5JSEjI9z6HgsrfZbFw4UJ27tzJ4MGD/XaMvJKQkMDatWspXrw4TZs2tTucfJHZ30diYiJbt24FuCY2Nvb3y9lfkWxWmjBhAp06dSIuLg6wCquoJAal7PDmm2/y4Ycf8u9//9vuUHwWHR0dMInBH4pks1K3bt1YsWIF119/vd2hKFUkDBkyxO4QLtvVV19tdwj5Km2Uh7xSJGoO+/fvp3fv3ogIANWqVePzzz/P0NGjCregoCDP3SNKqfRSUlKyvFEhN4pEzWHz5s0sXLiQOnXq8OKLL9odjvKTkJAQzp07x9mzZwkODsbhcJCcnBxQL3XJjpbFRYFUFi6Xi5SUFFJSUvJ01OJCW3P49ddfPbeVderUiS+++IIXXnjB5qiUv0VGRuJ0Oj3V5/x4UrSw0LK4KJDKwuFw4HQ68/zW/EJZc1ixYgU9e/akX79+vPzyywDceOONNkel8sulV0eB9FKXnGhZXKRlcWX8lhyMMUHAe0ADIBHoJyK7vJY/CDwEXABGi8hXvu47NjaWmJgY/vWvf+Vx1EoppcC/zUq3A8VEpCnwLDA+bYEx5ipgENAcuBV41RiT4wMMaeOoFC9enK+++orOnTv7I26llAp4/mxWagF8AyAia40xjbyW/QtYJSKJQKIxZhdwHbA+i30FA3z22WeekVQDnfc49IFOy+IiLYuLtCzw7pS/7IGY/JkcSgJ/eU2nGGNCRORCJsv+BjIOcXjRPwAGDBjAL7/8kueBFkbupx4VWhbetCwu0rJI5x/AZfXS+zM5nAa8u8+D3Ikhs2WRwJ/Z7Gs9cCNwGEjJyyCVUqoIC8ZKDFm1ymTJn8lhFdAZmGOMaQJs8Vr2IzDGGFMMCAOigSzTfGxsbCKw0o+xKqVUUZWr+3r9NvCe191K1wEO4H6gI7BLROa771bqj9Up/oqIfO6XQJRSSl22QjEqq1JKqfxVaJ+QVkop5T+aHJRSSmWgyUEppVQGBW5sJX8Ou1GY+FAOTwB3uye/FpGX8j/K/JFTWXitswD4QkQm5X+U+cOH34sOwAtYN4HEA4+KSJHsWPShLJ4EegGpWDe9zLMl0HxkjGkMjBWR1pfM7wyMxDpvThORD3LaV0GsOeT5sBuFVHblUBPoDTQDmgC3GGOusyXK/JFlWXgZDZTJ16jskd3vRSTwOnCbiDQGfgfK2xFkPsmuLEoDg4GmwC3ABFsizEfGmKeBKUCxS+aHAm9hlUMroL8xplJO+yuIySHdsBtApsNuiMhfQNqwG0VRduWwH2gvIinuq8JQ4Hz+h5hvsisLjDF3Yl0dfpP/oeW77MqiGdbzROONMSuAP0TkWP6HmG+yK4sEYC8Q4f6Xmu/R5b/dQLdM5kdjPUJwSkSSsJ4Za5nTzgpicsh02I0sluU07EZhlmU5iEiyiBw3xjiMMW8AP4nITluizB9ZloUxpj5W08FIOwKzQXZ/H+WBNsAzQAfgcWNMUX4dYnZlAdZF1DZgIzAxPwOzg/tZseRMFuXqvFkQk0NeDrtRmGVXDrifLp/pXueRfI4tv2VXFv8GKgNLgfuAIcaY9vkbXr7KrixOAOtF5IiInAGWA0X5RerZlUUHrGEjrgGqAbcbYwJ1jP9cnTcLYnJYhfUkNVkMu3GjMaaYMaYUOQy7UchlWQ7GGAfwBbBJRB4SkaI+3lSWZSEiT4tIY3cH3IfAmyJSlJuXsvv72AjUN8aUd19BN8G6ci6qsiuLU8A5IFFEzmOdDEvne4QFw3agjjGmrDHGidWktCanjQrc3UrAPOBmY8xq3MNuGGOGcHHYjYnACqzE9pz7B18UZVkOWINptQLC3HenAAwTkRx/4IVUtr8T9oaW73L6+xgGfOted46IFNWLJ8i5LNoBa40xqVjt7ItsjDXfGWN6ASVE5H13uXyLdd6cJiIHc9peh89QSimVQUFsVlJKKWUzTQ5KKaUy0OSglFIqA00OSimlMtDkoJRSKoOCeCurCkDGmBrATjLel99ZRPZnsc2LACLy4hUc9z7gTWCfe1Y48APwiPdDhz7uaxSwwX0b5TIRaeOe/7OIXNHDaMaY74EqwBn3rJLAHqC3iPyRzXb9gb9FZNaVHF8FHk0OqiA5dKUn0VyaLyL3ARhjgoHvgUeBty9nJyLiPYRHa6/5efWd+onI9+AZkfQzYAjWcBlZaYb1fZS6LJocVIHnHj/pP0AJoCIwXkQmei0PBaYB9d2z3hORD9wjT04GqmINvDZMRBZndywRSXE/VBXl3vf9wJOAC2sI7IFYw0NndrwPsU7EMe5t14lIY2NM2uCI+4CGIvKHMaYs1tP91YGbgFHudX4DHhSREzkUSwTWWErr3Mfq4Y4z3P2vH+AEugBtjTGHgZ8vtzxU4NI+B1WQXG2M+dnr31D3/H5Y7+64AWtguTGXbNcMKCsiDYF2WEO6g3XlP01EYrFOkpPdw1pnyRhTDmtcnlXGmH8CzwGtROSfWCN9vpDN8QAQkUHu/xt7zbsAxAE93LO6A/+HNaTDa8Ct7v19C4zNIrwpxphN7hP9Wqwnft9y1yIexhqqu4F7f0PdJ/75wEgR+TY35aECl9YcVEGSVbPSk0B799AQ12HVILxtBYwx5lvgay42s7QD6rr7AsC6Mq+FdQXtrYsx5mesIRiCgLnALKympS+9ruLfB/6HdfLN7Hg5mYH1XoF3gHuAEUBjrIHhlhljwBoa5WQW2/cTke+NMc2Az7Fe8pSEFcwdQGdj7aQ1kNl4W76Wh1KaHFShMAdrILUvgdlcfAMeACJywhhTD7gZayC2je7pYKCtiJwEMMZcDWTWeevpc/DmviL35gBCsjletkRkg3vwsxuAKiKy2hjTFVgpIl3cxyxG+hE0M9vPavcYYx8ZYxpgvdxlPVbyWQ5sxmr+upSv5aGUNiupQuFmrKaRL7AGHEzrOMb9uQvwMdZrQgdh3dFTFWsY70fc61yLddIsfhnH/R6rVlHWPf0g1hV+Vsfzdum7BdLMxGr3n+2eXgc09XrvwvNYb3PLyZtY/Q4PY/WPpAKvYH3nDliJAKzXQqbFcaXloQKIJgdVGLwIrDTGbMR6PezvWOP0p1mINTzzL1jDus8VkS3AY0ATY8xm4FOgj4j87etBRWQz8CrwgzFmB1b/wIhsjuftC2CTuybg7WOsdyx87D7GEaAvMMcYswWrM/tJH2JLxOoPeQHrDWA/Azuwhu0+g9XRDbAYGO5+W94VlYcKLDoqq1JKqQy05qCUUioDTQ5KKaUy0OSglFIqA00OSimlMtDkoJRSKgNNDkoppTLQ5KCUUiqD/w8GJ39adX9QtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_classifiers = [\n",
    "    BernoulliNB(),\n",
    "    MultinomialNB(),\n",
    "    LogisticRegression(),\n",
    "    LogisticRegressionCV(),\n",
    "    MLPClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier()\n",
    "]\n",
    "\n",
    "binary_classifiers = [\n",
    "    LinearSVC(),                   \n",
    "    SVC(),                     \n",
    "    SGDClassifier(),              \n",
    "    PassiveAggressiveClassifier(), \n",
    "    RidgeClassifier(),             \n",
    "    RidgeClassifierCV()\n",
    "]\n",
    "\n",
    "for classifier in multi_classifiers:\n",
    "    oz = ROCAUC(classifier)\n",
    "    oz.fit(X_train, y_train)\n",
    "    oz.score(X_test, y_test)\n",
    "    g = oz.poof()\n",
    "    \n",
    "for classifier in binary_classifiers:\n",
    "    oz = ROCAUC(classifier, micro=False, macro=False, per_class=False)\n",
    "    oz.fit(X_train, y_train)\n",
    "    oz.score(X_test, y_test)\n",
    "    g = oz.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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